For a 1+1 dimensional Vlasov model with Krook collisions, a Carleman linearization quantum algorithm has polynomially worse complexity than classical finite difference methods and requires unphysically large collision rates to provably converge.
Encoding of linear kinetic plasma problems in quantum circuits via data compression
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abstract
We propose an algorithm for encoding of linear kinetic plasma problems in quantum circuits. The focus is made on modeling electrostatic linear waves in one-dimensional Maxwellian electron plasma. The waves are described by the linearized Vlasov-Amp\`ere system with a spatially localized external current that drives plasma oscillations. This system is formulated as a boundary-value problem and cast in the form of a linear vector equation $A\psi = b$ to be solved by using the quantum signal processing algorithm. The latter requires encoding of the matrix $A$ in a quantum circuit as a subblock of a unitary matrix. We propose how to encode $A$ in a circuit in a compressed form and discuss how the resulting circuit scales with the problem size and the desired precision.
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Solving the Nonlinear Vlasov Equation on a Quantum Computer
For a 1+1 dimensional Vlasov model with Krook collisions, a Carleman linearization quantum algorithm has polynomially worse complexity than classical finite difference methods and requires unphysically large collision rates to provably converge.